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English(EN) Mitigating Data Scarcity in Psychological Defense Classification with Context-Aware Synthetic Augmentation

新方法通过合成数据提升心理防御分类效果

研究人员开发了一种新方法,以改善文本中心理防御机制的分类,解决了数据稀疏和类别不平衡的常见问题。他们的方法结合了上下文感知的合成数据增强技术和一种混合分类模型,该模型整合了语言表征和临床特征。该框架在 PsyDefDetect 共享任务中进行了测试,与现有方法相比,在准确率和宏 F1 分数上均有显著提高,为低资源环境树立了新的基准。 AI

影响 为低资源环境下的心理防御机制分类树立了新的基准。

排序理由 该集群包含一篇详细介绍文本分类新方法的学术论文。

在 Hugging Face Daily Papers 阅读 →

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新方法通过合成数据提升心理防御分类效果

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该集群包含一篇详细介绍文本分类新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Huy-Hieu Pham ·

    利用上下文感知合成增强技术缓解心理防御分类中的数据稀疏性问题

    Psychological defense mechanisms (PDMs) are unconscious cognitive processes that modulate how individuals perceive and respond to emotional distress. Automatically classifying PDMs from text is clinically valuable but severely hindered by data scarcity and class imbalance, challe…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    利用上下文感知合成增强技术缓解心理防御分类中的数据稀疏性问题

    Psychological defense mechanisms (PDMs) are unconscious cognitive processes that modulate how individuals perceive and respond to emotional distress. Automatically classifying PDMs from text is clinically valuable but severely hindered by data scarcity and class imbalance, challe…